 Live from Miami, Florida, it's theCUBE. Covering IBM's data in AI forums. Brought to you by IBM. Welcome back to the port of Miami, everybody. You're watching theCUBE, the leader in live tech coverage. And we're here covering the IBM data and AI forum. Rob Thomas is here. He's the general manager for data and AI at IBM. Great to see you again, Rob. Dave, great to see you here in Miami. Beautiful week here in the beach area. It's nice. Yeah, this is quite an event. I mean, I had thought it was going to be like roughly a thousand people that's oversold. There's 17, more than 1700 people here. This is a learning event, right? I mean, people here, they're here to absorb best practice, you know, learn technical hands-on presentations. Tell us a little bit more about how this event has evolved. It started as a really small training event, like you said, which goes back five years. And what we saw was people, they weren't looking for the normal kind of conference. They wanted to be hands-on. They wanted to build something. They wanted to come here and leave with something they didn't have when they arrived. So it started as our little small builder conference. And now somehow it continues to grow every year, which we're very thankful for. And we continue to kind of expand, add sessions. We've had to add hotels this year, so it's really taken off. It's great. Your title has two of the three superpowers, data, AI, and of course, cloud is the third superpower, which is part of IBM's portfolio. But people want to apply those superpowers and you use that metaphor in your keynote today to really transform their business. But you pointed out that only about, AI is only four to 10% penetrated within organizations. And you talked about some of the barriers there, but there's a real appetite to learn, isn't there? There is. Let's go talk about the superpowers for a bit. AI does give employees superpowers because they can do things now they couldn't do before. But you think about superheroes, they all have an origin story. They always have somewhere where they started and applying AI in an organization, it's actually not about doing something completely different. It's about extenuating what you already do. Doing something massively better that's kind of in your DNA already. So we're encouraging all of our clients this week, like use the time to understand what you're great at, what your value proposition is, and then how do you use AI to extenuate that? Because your superpower is only going to last if it starts with who you are as a company or as a person. Who was your favorite superhero as a kid? Let's see, I was kind of into the whole hall of justice superman, that kind of thing. That was probably my cartoon. I was a Batman guy and the reason I love Batman was because of all the combination of tech, which kind of reminds me of what's happening here today in the marketplace. People are taking data, they're taking AI, they're applying machine intelligence to that data to create new insights, which they couldn't have before. But to your point, there's an issue with the quality of data, and there's a skills gap as well. So let's start with the data quality problem. Describe that problem and how are you guys attacking it? Your AI is only as good as your data. I'd say that's the fundamental problem. And organization we work with, 80% of the projects get slowed down or they get stopped because the company has a data problem. That's why we introduced this idea of the AI ladder, which is all of the steps that a company has to think about for how they get to a level of data maturity that supports AI. So how they collect their data, organize their data, analyze their data, and then ultimately begin to infuse AI into business processes. So every organization needs to climb that ladder, and they're all different spots. So for some it might be we've got to focus on organization, a data catalog. For others it might be we've got to do a better job of data collection, data management. That's for every organization to figure out, but you need a methodical approach to how you attack the data problem. So I want to ask you about the AI ladder. So you could have these verbs. The verbs overlay on building blocks. I went back to some of my notes in the original AI ladder conversation that you introduced a while back. It was data and information architecture at the base and then building on that, analytics, machine learning, AI. And then now you've added the verbs, collect, organize, analyze, and infuse. Should we think of this as a maturity model or building blocks and verbs that you can apply depending on where you are in that maturity model? I would think of it as building blocks and a methodology, which is you've got to decide, should we focus on our data collection and doing that right? Is that our weakness? Or is it data organization? Or is it the sexy stuff, the AI, the data science stuff? This is just a tool to help organizations organize themselves on what's important. I ask every company I visit, do you have a data strategy? You wouldn't believe the looks you get when you ask that question. You get either, well, she's got one, he's got one. So we got seven, or you get, no, we've never had one. Or, hey, we just hired a CDO, so we hope to have one. But we use the AI ladder just as a tool to encourage companies to think about your data strategy. Should, do you think, in the context, I want to follow up on that in the data strategy? Because you see a lot of tactical data strategies. Well, we use data for this initiative or that initiative, maybe in sales or marketing or maybe in R&D. Increasingly, are organizations developing and should they develop a holistic data strategy or should they try to just get kind of quick wins? What are you seeing in the marketplace? It depends on where you are in your maturity cycle. I do think it behooves every company to say, we understand where we are and we understand where we want to go. That could be the high level data strategy. What are our focus and priorities going to be? Once you understand focus and priorities, the best way to get things into production is through a bunch of small experiments to your point. So I don't think it's an either or, but I think it's really valuable to have an overarching data strategy. And I recommend to companies, think about a hub and spoke model for this. Have a centralized chief data officer, but your business units also need a chief data officer. So strategy in one place, execution in another. There's a best practice to going about this, but it starts with the strategy. It connects the dots there. You asked the question, what is AI? You get that question a lot. And you said it's about predicting, automating and optimizing. Can we sort of unpack that a little bit? What's behind those three items? People overreact to hype on topics like AI and they think, well, I'm not ready for robots or I'm not ready for self-driving vehicles. Like those may or may not happen. Don't know, but AI is, let's think more basic. It's about can we make better predictions as a business? Every company wants to see a future. They want the proverbial crystal ball. AI helps you make better predictions if you have the data to do that. It helps you automate tasks, automate the things that you don't want to do. There's a lot of work that has to happen every day that nobody really wants to do. Use software to automate that. And this is about optimization. How do you optimize processes to drive greater productivity? So this is not black magic. This is not some far-off thing. We're talking about basics. Better predictions, better automation, better optimization. Now, interestingly, you use the term black magic because a lot of AI is black box. And IBM has always made a point of we're trying to make AI transparent. We talk a lot about taking the bias out or at least understanding when bias makes sense, when it doesn't make sense. Talk about the black box problem and how you're addressing that. Starts with one simple idea. AI is not magic. I say that over and over again. This is just computer science. Then you have to look at what are the components inside the proverbial black box? With Watson, we have a few things. We've got tools for clients that want to build their own AI. So think of it as a toolbox. You can choose, do you want a hammer, do you want a screwdriver, do you want a nail? You can go build your own AI using Watson. We also have applications. So it's basically an end user application that puts AI into practice. Things like Watson Assistant to virtual, create a virtual agent for customer service or Watson Discovery or things like open pages with Watson for governance, risk and compliance. So AI for Watson is about tools that you want to build your own applications. You just want to consume an application. And we've also got embedded AI capabilities. So you can pick up Watson and put it inside of any software product in the world. You also mentioned that Watson was built with a lot of open source components, which a lot of people might not know. What's behind Watson? 85% of the work that happens in Watson today is open source. Most people don't know that. It's Python, it's R, it's deploying into TensorFlow. What we've done, where we focused our efforts is, how do you make AI easier to use? So we've introduced auto AI into Watson Studio. So if you're building models in Python, you can use auto AI to automate things like feature engineering, algorithm selection, the kind of thing that's hard for a lot of data scientists. So we're not trying to create our own language. We're using open source, but then we make that better so that a data scientist can do their job better. So again, come back to AI adoption. We talked about three things, quality, trust and skills. We talked about the data quality piece. We talked about the black box challenge. Let's talk about skills. You mentioned there's a 250,000 person gap right now in terms of data science skills. How is IBM approaching, how are customers and IBM approaching closing that gap? So thinking of that, but this is in basic economic terms. So we have a supply-demand mismatch. Massive demand for data scientists, not enough supply. The way that we address that is twofold. One is we've created a team called Data Science Elite. They've done a lot of work for the clients that were on stage with me who help a client get to their first big win with AI. It's that simple. We go in for four to six weeks. It's an elite team. It's not a long project. We're going to get you to your first success. Second piece is the other way to solve demand and supply mismatch is through automation. So I talked about auto AI, but we also do things like using AI for building data catalogs, metadata creation, data matching, so making that data prep process automated through AI can also help that supply-demand mismatch. So the way that you solve this is we put skills on the field to help clients and we do a lot of automation in software. That's how we can help clients navigate this. So the Data Science Elite team, I love that concept because we first picked up on it a couple of years ago at least. It's one of the best freebies in the business. It is. But of course you're doing it with the customers that you want to have deeper relationships with and I'm sure it leads to follow on business. What are some of the things that you're most proud of from the Data Science Elite team that you might be able to share with us? The client stories are amazing. I talked in the keynote about origin stories. Royal Bank of Scotland automating 40% of their customer service now. Customer stats going up 20% because they put their customer service reps on those hardest problems. That's Data Science Elite helping them get to a first success. Now they scale it out. I had Wonderman Thompson on stage. Part of the big WPP, the big advertising agency. They're using AI to comb through customer records. They're using auto AI. That's the Data Science Elite team that went in for literally four weeks and gave them the confidence that they could then do this on their own once we left. We got countless examples where this team has gone in for very short periods of time and clients don't talk about this because they have to. They talk about it because they're like we can't believe what this team did. So we're really excited by that. The interesting thing about the RBS example to me, Rob, was that you basically applied AI to remove a lot of these mundane tasks that weren't really driving value for the organization. And then RBS was able to shift the skill sets to more strategic areas. We always talk about that, but I love the example. So can you talk a little bit more about really where that shift was? What did they go from and what did they apply to and how it impacted their business to say improvement? I think it was 20% improvement in NPS, but give us a pull on that. Well, what they realized is that the inquiries they had coming in were of two categories. There were ones that were really easy, there were ones that were really hard. And they were spreading those equally among their employees. So what you get is a lot of unhappy customers. And then once they said we can automate all the easy stuff, we can put all of our people in the hardest things, customers sat, shot through the roof. Now, what does a virtual agent do? Let's decompose that a bit. We have a thing called intent classification as part of Watson Assistant, which is it's a model that understands customer intent. And it's trained based on the data from Royal Bank of Scotland. So this model after 30 days is not very good. After 90 days, it's really good. After 180 days, it's excellent. Because at the core of this is we understand the intent of customers engaging with them. We use natural language processing. It really becomes a virtual agent that's done all in software. And you can only do that with things like AI. And what is the role of the human element in that? How does it interact with that virtual agent? Is it sort of an attended agent or is it unattended? What is that like? So it's two pieces. So for the easiest stuff, no humans needed. We just go do that in software. For the harder stuff, we've now given the RBS customer service agents superpowers because they've got Watson Assistant at their fingertips. The hardest thing for a customer service agent is normally finding the right data to solve a problem. Watson Discovery is embedded in Watson Assistant so they can basically comb through all the data in the bank to answer a question. So we're giving their employees superpowers. So on one hand, it's augmenting the humans. In another case, we're just automating the stuff that humans don't want to do in the first place. I want to shift gears a little bit. Talk about Red Hat and OpenShift. Obviously huge acquisition last year. $34 billion, next chapter kind of an IBM strategy. A couple of things you're doing with OpenShift. Watson is now available on OpenShift. So that means you're bringing Watson to the data. I want to talk about that. And then CloudPak for data also on OpenShift. So what has that Red Hat acquisition done for you? Obviously you know a lot about M&A, but now you're in the position of you've got to take advantage of that and you are taking advantage of that. So give us an update on what you're doing there. So look at the cloud market for a moment. You've got around $600 billion of opportunity of traditional IT on premise. You've got another $600 billion public clouds, dedicated clouds and you've got about 400 billion that's private cloud. So the cloud market is fragmented between public, private and traditional IT. The opportunity we saw was if we can help clients integrate across all of those clouds, that's a great opportunity for us. What Red Hat OpenShift is, it's a liberator. It says write your application once, deploy them anywhere because you build them on Red Hat OpenShift. Now we've brought CloudPak for data. Our data platform onto Red Hat OpenShift certified on that. Watson now runs on Red Hat OpenShift. What that means is you can have the best data platform, the best AI and you can run it on Google, AWS, Azure, your own private cloud. You can get the best AI with Watson from IBM and run it in any of those places. So the reason why that's so powerful is because you're able to bring those capabilities to the data without having to move the data around. I think it was Jennifer showed an example or no, maybe it was Dylan. It was Jennifer. Yeah, she was showing virtualizing those data sets. Yeah, and so the beauty of that is I didn't have to move any data. Talk about the importance of not having to move that data. And then I want to understand what the client prerequisite is to really take advantage of that. This is one of the greatest inventions out of IBM research in the last 10 years that hasn't gotten a lot of attention, which is data virtualization. Data federation, traditional federation has been around forever. The issue was it doesn't perform. Our data virtualization performs 500% faster than anything else in the market. So what Jennifer showed in that demo was I'm training a model and I'm going to virtualize the data set from Redshift on AWS and on-premise repository, a MySQL database. We don't have to move the data. We just virtualize those data sets into Cloudpack for data and then we can train the model in one place. Like this is actually breaking down data silos that exist in every organization and it's really unique. There was a very cool demo because what she did is she was pulling data from different data stores, doing joins. It was a healthcare application, really trying to understand where the bias was. Peeling the onion, is bias, sometimes bias is okay, right? You just got to know whether or not it's actionable. And so that was very cool without having to move any of the data. What is the prerequisite for clients? What do they have to do to take advantage of this? Start using Cloudpack for data. We've got something on the web called Cloudpack Experiences. Anybody can go try this in less than two minutes. I just say, go try it, because Cloudpack for data will just insert right onto any public cloud you're running or in your private cloud environment. You just point to the sources and it will instantly begin to start to create what we call schema folding. So a version of the schema from your source writing Cloudpack for data. This is like instant access to your data. It sounds like magic. It is. Okay, last question. What are the big takeaways you want people to leave this event with? We are trying to inspire clients to give AI a shot. Adoption is four to 10%. For what is the largest economic opportunity we'll ever see in our lives? That's not an acceptable rate of adoption. So we're encouraging everybody, go try things. Don't do one AI experiment. Do a hundred AI experiments in the next year. And if you do a hundred, 50 of them probably won't work. This is where you have to change the cultural idea as it comes into it. Be prepared that half of them aren't going to work, but then for the 50 that do work, then you double down, then you triple down. Everybody will be successful with AI if they have this iterative mindset. Yeah, and with Cloud it's very inexpensive to actually do those experiments. Rob Thomas, thanks so much for coming on theCUBE. It was great to see you. Thank you, great to see you. All right, keep it right there, but we'll be back with our next guest. Right after this short break, we're here from Miami at the IBM AI data forum. We'll be right back.